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A Matrix-Inverse-Free wmmse algorithm to MISO Beamforming Based on Quasi-Newton

IEEE COMMUNICATIONS LETTERS 2024年第12期28卷 2809-2813页

作者： Sun, Mingjun Li, Zeng Wu, Shaochuan Ma, Ruofei Jiang, Litong Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China State Key Lab Mobile Network & Mobile Multimedia T Shenzhen 518055 Peoples R China

This letter propose a quasi-Newton based weighted minimum mean square error (wmmse) algorithm without matrix inverse to solve the weighted sum rate (WSR) maximization problem in multi-user multi-input single-output (MU-MISO) beamforming. On one hand, the quasi-Newton method can replace the first-order optimal condition to solve the extremum problem of the convex quadratic function, without involving matrix inverse. One the other hand, compared to projected gradient descent (PGD) approach, it can achieve a faster convergence under the guidance of approximate Hessian matrix and avoid performance loss under the condition of high transmit power. Furthermore, a learning strategy is adopted to replace the linear searching process to obtain the optimal step size that satisfies the Wolfe condition. Simulation results validate that the proposed algorithm can achieve the same performance as wmmse, but with a reduced computation complexity.

关键词： Precoding Heuristic algorithms Computational complexity Newton method Iterative algorithms Convergence Approximation algorithms Vectors Downlink Array signal processing Downlink beamforming quasi-Newton wmmse algorithm matrix-inverse-free

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Coordinated Sum-Rate Maximization in Multicell MU-MIMO With Deep Unrolling

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2023年第4期41卷 1120-1134页

作者： Schynol, Lukas Pesavento, Marius Tech Univ Darmstadt Commun Syst Grp D-64283 Darmstadt Germany

Coordinated weighted sum-rate maximization in multicell MIMO networks with intra- and intercell interference and local channel state at the base stations is recognized as an important yet difficult problem. A classical, locally optimal solution is obtained by the weighted minimum mean squared error (wmmse) algorithm which facilitates a distributed implementation in multicell networks. However, it often suffers from slow convergence and therefore large communication overhead. To obtain more practical solutions, the unrolling/unfolding of traditional iterative algorithms gained significant attention. In this work, we demonstrate a complete unfolding of the wmmse algorithm for transceiver design in multicell MU-MIMO interference channels with local channel state information. The resulting architecture termed GCN-wmmse applies ideas from graph signal processing and is agnostic to different wireless network topologies, while exhibiting a low number of trainable parameters and high efficiency w.r.t. training data. It significantly reduces the number of required iterations while achieving performance similar to the wmmse algorithm, alleviating the overhead in a distributed deployment. Additionally, we review previous architectures based on unrolling the wmmse algorithm and compare them to GCN-wmmse in their specific applicable domains.

关键词： Deep unrolling deep unfolding wmmse algorithm coordinated downlink beamforming multiuser MIMO multicell network graph convolutional neural network

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SVDNet: Deep Power Control for Multiuser MIMO 97

SVDNet: Deep Power Control for Multiuser MIMO

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97th IEEE Vehicular Technology Conference (VTC-Spring)

作者： Maiti, Ritabrata Madhukumar, A. S. Ernest, Tan Zheng Hui Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore ASTAR Adv Remanufacturing & Technol Ctr ARTC Singapore Singapore

ISBN: (纸本)9798350311143

In this work, we propose SVDNet, a novel deep learning (DL) architecture that utilizes singular value decomposition (SVD), for transmit power control in a multiuser multiple input multiple output (MU-MIMO) system. We propose a novel method of training SVDNet in a supervised manner for the power control task by using binary cross-entropy loss functions. SVDNet requires fewer computations than traditional power control algorithms such as weighted minimum mean squared error (wmmse). Our simulation results show that the proposed SVDNet provides over a 50% increase in sum-rate performance as compared to similar supervised DL-based power control schemes while being significantly more computationally efficient than wmmse.

关键词： Deep learning singular value decomposition power control resource management wmmse algorithm

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Power Allocation Optimization Design for D2D Cellular Networks Based on Graph Neural Networks 23

Power Allocation Optimization Design for D2D Cellular Networ...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Yu, Lisu Yuan, Junjun Lv, Xinxin Wang, Zhen Cai, Donghong Dong, Zhicheng School of Information Engineering Nanchang University Nanchang330031 China College of Information Science and Technology Jinan University Guangzhou510632 China College of Information Science Technology Tibet University Lhasa850000 China

ISBN: (纸本)9798350325959

This study focuses on the co-frequency interference issue between cellular users (CUs) and device-to-device users (DUs)., with the objective of maximizing the system sum-rate, in which a power allocation optimization strategy based on graph neural networks (GNN) is proposed. Leveraging the relative distances between transmitting and receiving nodes, a distance-based grouping and alternating channel allocation approach is devised. The aim is to identify suitable cellular user-shared channel resources for the device-to-device (D2D) source users within the system. In addition, this paper utilizes graph neural networks to address non-linear optimization problems, effectively overcoming the limitations of traditional optimization algorithms with low computational efficiency. Through simulation results, the efficacy of the proposed method in enhancing the cellular network's sum-rate is validated. In comparison to the weighted minimum mean square error (wmmse) algorithm, the proposed approach demonstrates superior performance, such as higher generalization capability and faster response times. © 2023 IEEE.

关键词： channel allocation D2D cellular network GNN power allocation wmmse algorithm

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A Deep Learning based Resource Allocation algorithm for Variable Dimensions in D2D-Enabled Cellular Networks

A Deep Learning based Resource Allocation Algorithm for Vari...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Pei, Errong Yang, Guangcai Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Peoples R China

ISBN: (纸本)9781728173276

Optimization algorithms play an important role in resource allocation problems. However, the algorithms are difficult to be applied in practice due to the high complexity. Some deep neural networks (DNNs) are thus proposed to approach the traditional algorithms, which can realize real-time resource allocation. However, the DNNs is designed for invariable dimensions. Therefore, it remains unclear whether the neural network under variable dimensions can still approach the traditional algorithm. Furthermore, it still remains unclear how to train the neural network for variable dimensions. In this work, we propose a deep learning based power control scheme for variable D2D pairs, where low-dimensional inputs are preprocessed by zero-padding, and several hybrid training methods are proposed. Through a large number of experimental simulations, it is proved that the preprocessing method can better deal with the variable dimensions problem without introducing new interference. The fully connected DNN trained by different-dimensional data is proved to be the closest to the traditional algorithms.

关键词： Optimization algorithm deep neural network interference management wmmse algorithm D2D communication

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Cooperative Rate Splitting for MISO Broadcast Channel With User Relaying, and Performance Benefits Over Cooperative NOMA

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IEEE SIGNAL PROCESSING LETTERS 2019年第11期26卷 1678-1682页

作者： Zhang, Jian Clerckx, Bruno Ge, Jianhua Mao, Yijie Xidian Univ ISN State Key Lab Xian 710126 Peoples R China Imperial Coll London London SW7 2AZ England Univ Hong Kong Hong Kong Peoples R China

Due to its promising performance in a wide range of practical scenarios, Rate-Splitting (RS) has recently received significant attention in academia for the downlink of communication systems. In this letter, we propose and analyse a Cooperative Rate-Splitting (CRS) strategy based on the three-node relay channel where the transmitter is equipped with multiple antennas. By splitting user messages and linearly precoding common and private streams at the transmitter, and opportunistically asking the relaying user to forward its decoded common message, CRS can efficiently cope with a wide range of propagation conditions (disparity of user channel strengths and directions) and compensate for the performance degradation due to deep fading. The precoder design and the resource allocation are optimized by solving the Weighted Sum Rate (WSR) maximization problem. Numerical results demonstrate that our proposed CRS scheme can achieve an explicit rate region improvement compared to its non-cooperative counter-part and other cooperative strategies (such as cooperative NOMA).

关键词： Rate-splitting resource allocation relay broadcast channel rate region wmmse algorithm

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Learning to Optimize: Training Deep Neural Networks for Interference Management

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第20期66卷 5438-5453页

作者： Sun, Haoran Chen, Xiangyi Shi, Qingjiang Hong, Mingyi Fu, Xiao Sidiropoulos, Nicholas D. Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA Tongji Univ Sch Software Engn Shanghai Peoples R China Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA Univ Virginia Dept Elect & Comp Engn Charlottesville VA 22904 USA

Numerical optimization has played a central role in addressing key signal processing (SP) problems. Highly effective methods have been developed for a large variety of SP applications such as communications, radar, filter design, and speech and image analytics, just to name a few. However, optimization algorithms often entail considerable complexity, which creates a serious gap between theoretical design/analysis and real-time processing. In this paper, we aim at providing a new learning-based perspective to address this challenging issue. The key idea is to treat the input and output of an SP algorithm as an unknown nonlinear mapping and use a deep neural network (DNN) to approximate it. If the nonlinear mapping can be learned accurately by a DNN of moderate size, then SP tasks can be performed effectively-since passing the input through a DNN only requires a small number of simple operations. In our paper, we first identify a class of optimization algorithms that can be accurately approximated by a fully connected DNN. Second, to demonstrate the effectiveness of the proposed approach, we apply it to approximate a popular interference management algorithm, namely, the wmmse algorithm. Extensive experiments using both synthetically generated wireless channel data and real DSL channel data have been conducted. It is shown that, in practice, only a small network is sufficient to obtain high approximation accuracy, and DNNs can achieve orders of magnitude speedup in computational time compared to the state-of-the-art interference management algorithm.

关键词： Optimization algorithms approximation deep neural networks interference management wmmse algorithm

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Rate-Splitting for Multi-Antenna Non-Orthogonal Unicast and Multicast Transmission 19

Rate-Splitting for Multi-Antenna Non-Orthogonal Unicast and ...

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19th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Mao, Yijie Clerckx, Bruno Li, Victor O. K. Univ Hong Kong Hong Kong Hong Kong Peoples R China Imperial Coll London London England

ISBN: (纸本)9781538635124

In a superimposed unicast and multicast transmission system, one layer of Successive Interference Cancellation (SIC) is required at each receiver to remove the multicast stream before decoding the unicast stream. In this paper, we show that a linearly-precoded Rate-Splitting (RS) strategy at the transmitter can efficiently exploit this existing SIC receiver architecture. By splitting the unicast messages into common and private parts and encoding the common parts along with the multicast message into a super-common stream decoded by all users, the SIC is used for the dual purpose of separating the unicast and multicast streams as well as better managing the multi-user interference between the unicast streams. The precoders are designed with the objective of maximizing the Weighted Sum Rate (WSR) of the unicast messages subject to a Quality of Service (QoS) requirement of the multicast message and a sum power constraint. Numerical results show that RS outperforms existing Multi-User Linear-Precoding (MU-LP) and power-domain Non-Orthogonal Multiple Access (NOMA) in a wide range of user deployments (with a diversity of channel directions and channel strengths). Moreover, since one layer of SIC is required to separate the unicast and multicast streams, the performance gain of RS comes without any increase in the receiver complexity compared with MU-LP. Hence, in such non-orthogonal unicast and multicast transmissions, RS provides rate and QoS enhancements at no extra cost for the receivers.

关键词： non-orthogonal multicast and unicast transmission rate-splitting rate region wmmse algorithm

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